Despite ongoing research, lung cancers have remained among the most difficult to treat and account for the greatest number of cancer-related deaths worldwide. A suspected reason is the underlying molecular complexity of individual malignancies. Non-small cell lung cancers (NSCLC) account for approximately 85% of all lung cancers, yet research shows that these NSCLC tumors rarely share common cancer driver mutations, making it seemingly impossible to create a universally applicable model for researching the mechanisms of lunch cancer pathogenesis that provided translational results. For this reason, researchers and clinicians have begun investigating improved models including patient derived xenograft tumor models to study the underlying mechanisms or develop a more focused patient specific treatment regimen.

 

A recent study by Wang et al, “Molecular heterogeneity of non-small cell lung carcinoma patient-derived xenografts closely reflect their primary tumor”, published in the International Journal of Cancer, gives lung cancer research a clear path forward. What they found, by comparing the PDX derived data to matched patient tumors, was that PDXs established from lung cancers have significantly more in common with the characteristics of patient primary tumors than they do with established cell lines. Because most NSCLSC are histologically and genetically diverse, Wang et al aimed to establish that patient-derived tumor xenografts (PDX)from surgically resected non-small cell lung cancer are a good model which most closely reflects the heterogeneity of the original tumor.

 

Patient-derived xenografts have clearly emerged as the preferred model for translational lung cancer research, but it important to note that preliminary research also seems to indicate that NSCLC PDX models “respond similarly to pharmacological agents when compared to their matched tumors,” according to Wang et al. This suggests that a high throughput screening strategy, using a significantly larger population of PDX models, may be helpful in identifying broader responses to novel therapeutics in a pre-clinical setting.